proteins
STRUCTURE O FUNCTION O BIOINFORMATICS
PERSPECTIVES
Key challenges for the creation and
maintenance of specialist protein resources
Gemma L. Holliday,1* Amos Bairoch,2 Pantelis G. Bagos,3 Arnaud Chatonnet,4,5
David J. Craik,6 Robert D. Finn,7 Bernard Henrissat,8,9 David Landsman,10 Gerard Manning,11
Nozomi Nagano,12 Claire O’Donovan,7 Kim D. Pruitt,10 Neil D. Rawlings,7,13 Milton Saier,14
Ramanathan Sowdhamini,15 Michael Spedding,16 Narayanaswamy Srinivasan,17 Gert Vriend,18
Patricia C. Babbitt,1 and Alex Bateman7
1 Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94158
2 SIB—Swiss Institute of Bioinformatics, University of Geneva, Geneva, Switzerland
3 Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, 35100, Greece
4 INRA, Umr866 Dynamique Musculaire Et Metabolisme, Montpellier F-34000, France
5 Universite Montpellier, Montpellier, F-34000, France
6 Institute for Molecular Bioscience. The University of Queensland, Brisbane, Queensland 4072, Australia
7 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge Cb10 1SD, United
Kingdom
8 Architecture Et Fonction Des Macromolecules Biologiques, CNRS, Aix-Marseille Universite, Marseille, 13288, France
9 Department of Biological Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
10 National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20892
11 Department of Bioinformatics & Computational Biology, Genentech, 1 DNA Way, South San Francisco, California 98010
12 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan
13 Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, Cb10 1SD, United Kingdom
Grant sponsor: National Institutes of Health and National Science Foundation; Grant number: NIH R01 GM60595 and NSF DBI1356193 (to GLH and PCB); Grant
sponsor: the project “Integration of Data from Multiple Sources” (IntDaMus), which is implemented under the "ARISTEIA II" Action of the "OPERATIONAL PROGRAMME EDUCATION AND LIFELONG LEARNING" and cofunded by the European Social Fund (ESF) and National Resources (to PGB); Grant number: ANR-10BLAN-1216 (to AC); Grant sponsor: National Health & Medical Research Council (Australia); Grant numbers: APP1026501 and APP1028509; Grant sponsors: Australian
Research Council; Grant number: LP130100550 (to DJC); Grant sponsor: European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI; to AB
and RDF); Grant sponsor: Agence Nationale de la Recherche (CAZy database)—BIOMINES and BIP:BIP; Grant number: ANR-12-BIME-0006 and ANR-10-BINF-0304;
Grant sponsor: Intramural Research Program of the National Institutes of Health, National Library of Medicine (DL Histone Database; KDP RefSeq; work performed at
NCBI); Grant sponsors: Grant-in-Aid for Publication of Scientific Research Results; Grant number: 248047 (to NN); Grant sponsor: NN was organized by the Japan Society for the Promotion of Science (JSPS), and also has been supported by Commission for the Development of Artificial Gene Synthesis Technology for Creating Innovative Biomaterial from the Ministry of Economy, Trade and Industry (METI), Japan; Grant sponsor: National Institutes of Health (NIH); Grant number: U41HG007822
(to UniProtKB); Grant sponsor: EMBL-EBI’s involvement in UniProtKB comes from European Molecular Biology Laboratory (EMBL), the British Heart Foundation
(BHF); Grant number: RG/13/5/30112; Grant sponsor: Parkinson’s Disease United Kingdom (PDUK); Grant number: G-1307; Grant sponsor: NIH; Grant number:
U41HG02273; Grant sponsor: UniProtKB activities at the SIB are additionally supported by the Swiss Federal Government through the State Secretariat for Education,
Research, and Innovation SERI; Grant sponsor: NIH (to PIR’s UniProtKB activities); Grant number: R01GM080646, G08 LM010720, and P20GM103446; Grant sponsor:
National Science Foundation (NSF); Grant number: DBI-1062520; Grant sponsor: Department of Biotechnology, Government of India; Grant number: BT/01/COE/09/01
(to RS and NS); Grant sponsor: Wellcome Trust; Grant number: WT0077044/Z/05/Z (to Neil Rawlings); Grant sponsor: NIH; Grant number: GM077402 (to TCDB);
Grant sponsor: Swiss SERI (Secretariat for Education, Research, and Innovation) through the SIB—Swiss Institute of Bioinformatics; Grant sponsor: Wellcome Trust (to
The IUPHAR/BPS Guide to PHARMACOLOGY); Grant number: 099156/Z/12/Z; Grant sponsors: Industry sponsors (Servier), the British Pharmacological Society
(BPS), and IUPHAR; Grant sponsor: STW and EU; Grant number: KBBE-2011–5 289350 (to GV).
DL and KDP do not represent the opinion or official policy of the National Institutes of Health in this work.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
*Correspondence to: Gemma L. Holliday; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158. E-mail: gemma.
[email protected]
Received 12 January 2015; Revised 6 March 2015; Accepted 20 March 2015
Published online 27 March 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/prot.24803
C 2015 THE AUTHORS. PROTEINS: STRUCTURE, FUNCTION, AND BIOINFORMATICS PUBLISHED BY WILEY PERIODICALS, INC.
V
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14 Department of Molecular Biology, University of California at San Diego, La Jolla, California 92093
15 National Centre for Biological Sciences, TIFR, GKVK Campus, Bellary Road, Bangalore 560065, India
16 Chair NC-IUPHAR, Spedding Research Solutions SARL, 6 Rue Ampere, Le Vesinet 78110, France
17 Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
18 Centre for Molecular and Biomolecular Informatics (CMBI), Radboud University Medical Center, Geert Grooteplein Zuid 26-28, 6525 GA Nijmegen,
The Netherlands
ABSTRACT
As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus
increasing the importance of good access to these data that vary from the supplemental material of individual articles, all
the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an
important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins,
using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single
lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective
arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust
Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved
in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out
these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our
working together could enhance their accuracy, currency, and overall value.
Proteins 2015; 83:1005–1013.
C 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
V
Key words: specialist protein resource; key challenges; biocuration; longevity; big data; misannotation.
INTRODUCTION
With the advent of the technologies of the omics age,
there is far more data to manage, access, and understand
than ever before. As the data are far greater than any single
group of researchers can hope to ever cope with, repositories for these data are becoming increasingly important.
While it is simple to say: “I have data, therefore I shall create a database for it,” there are many challenges and hurdles in doing it such that the data can be retrieved and
studied effectively and efficiently. Recently, a small group
of leaders of web-accessible, knowledge-based, specialist
protein resources (SPRs) came together at a retreat sponsored by the Wellcome Trust to discuss the challenges they
face. The retreat was held at the Wellcome Trust Genome
Campus in Cambridge (UK) on August 11 and 12, 2014.
Although each SPR present represented some unique challenges and issues, it became clear that there were some
overarching challenges common to all of them. Together,
these can be combined into a single question: What makes
a database useful? Here, we discuss the top challenges that
emerged from this discussion, along with some of the ways
that were proposed to address them from the perspective
of the researchers at the retreat. The SPRs represented at
the retreat covered diverse communities, listed in Table I.
What are SPRs, and why do we need them?
The SPRs represented at the Wellcome Trust meeting
are just a tiny proportion of the SPRs available to
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researchers, but most are designed to perform a similar
function: to add value to the data available to researchers. SPRs cover a wide range of different types of protein.
Some are general and relate to all types of proteins (e.g.,
Pfam); others focus on specific types of proteins, for
example, transporter proteins (e.g., TCDB), receptor
proteins (e.g., GPCRDB), and enzymes (e.g., ExCatDB).
In all cases, data are available in many formats, including
the primary literature and associated supplementary
material, patents, and reference databases (e.g., RefSeq or
UniProtKB). An SPR can add value to data in many different ways, from simply collating it into levels of classification, to performing complex data analysis. The most
comprehensive list of SPRs can be found in the Nucleic
Acids Research Database issue and its associated Molecular Biology Database Collection,23 published annually in
January. In 2014 there were [mt]1500 databases listed in
the Molecular Biology Database Collection, which range
from comprehensive reference databases to resources that
focus on a single protein family, and everything in
between. The data types available in SPRs are just as
diverse, yet there are commonalities among them. All
proteins have a few features in common, namely their
amino acid sequence (and often also the associated
nucleic acid sequence) and the species from which they
come. Thus, most SPRs will contain either a nucleic or
amino acid sequence (or both), and at least a minimal
amount of metadata. The data that the SPRs add, however, is myriad and varied. Some will annotate the
Creation and Maintenance of Protein Resources
Table I
The SPRs and Major Databases That Participated in the Wellcome Trust retreat, Their URLs, and Primary References
Database name
Carbohydrate-Active Enzymes database (CAZy)
ConoServer database for conopeptides
CyBase database of cyclic proteins
ESTHER database (ESTerases and alpha/
beta-Hydrolase Enzymes and Relatives)
ExTopoDB database of experimentally derived
topological models of transmembrane proteins
EzCatDB database of Enzyme Catalytic Mechanisms
GPCRDB (G Protein-Coupled Receptors Database)
gpDB (a database of GPCRs, G-proteins, Effectors
and their interactions)
the Histone Database
the IUPHAR/BPS Guide to pharmacology
Kinase.com
the KinG database (a database of protein
kinases in genomes)
the MACiE Database (Mechanism, Annotation
and Classification in Enzymes)
MEROPS (the peptidase database)
neXtProt (knowledge resource on human proteins)
OMPdb (a database of b-barrel outer membrane
proteins from Gram-negative bacteria)
PASS2 database of structure-based sequence
alignments of protein structural domain superfamilies
Pfam
Reference Sequence (RefSeq) database
the Structure-Function Linkage Database (SFLD)
Transporter Classification Database (TCDB)
TIGRFAMs
UniProtKB
URL
Reference
www.cazy.org
http://www.conoserver.org/
http://www.cybase.org.au/
http://bioweb.ensam.inra.fr/ESTHER/general?what=index
1
2
3
4
http://bioinformatics.biol.uoa.gr/ExTopoDB/
5
http://ezcatdb.cbrc.jp/EzCatDB/
http://www.gpcr.org/7tm/
http://bioinformatics.biol.uoa.gr/gpDB/
6
7
8
http://genome.nhgri.nih.gov/histones/
http://www.guidetopharmacology.org/
http://kinase.com/
http://megha.garudaindia.in/king/index.jsp
9
10
11
http://www.ebi.ac.uk/thornton-srv/databases/MACiE
12
http://merops.sanger.ac.uk/
http://www.nextprot.org/
http://www.ompdb.org/
13
14
15
http://caps.ncbs.res.in/pass2/
16
http://pfam.xfam.org/
http://www.ncbi.nlm.nih.gov/refseq/
http://sfld.rbvi.ucsf.edu/
http://www.tcdb.org/
http://www.jcvi.org/cgi-bin/tigrfams/index.cgi
http://www.uniprot.org/
chemistry, such as the enzymatic reaction, cofactors, regulators, and so forth. Others add three-dimensional
information such as the PDB structure or active site
motifs. Some add disease information, such as disease
causing SNPs or polymeric forms; others look at the
kinetics of the reaction and small molecule binding. If
there is a study or data available in the primary literature
and a group of scientists interested in that field, the
chances are that there is an associated SPR. Thus, by
strengthening and expanding the realm of SPRs, we can
provide a richer and more diverse set of resources to the
research community, and accelerate the rate at which
individual results can be incorporated into interactive
databases for greater use.
Misannotation and data integrity
The foremost challenge to most SPRs is the issue of
the accuracy of data and its associated annotations, not
only in their own resources, but in those of others too.
Many different types of error can be found in data
resources, all of which present challenges for users, especially those unfamiliar with their specialized content. An
analysis done in 2009 on a relatively small set of highly
17
18
19
20
21
22
manually curated enzyme superfamilies24 showed that
some major public databases misidentified an average of
5–63% across the six superfamilies studied, usually by
“overannotation” of specific function when the evidence
only supports annotation of general functional properties. Some errors are relatively easy to identify through
automated processes and pipelines (e.g., MisPred,25
which identifies erroneous protein sequence function
predictions in public databases, usually in the form of
abnormal, incomplete and incorrect predictions). Others,
such as errors in the underlying scientific information
(e.g., if the protein sequence has translation errors or the
biochemical characterization is incomplete) are much
harder to find, especially as our knowledge continues to
grow so fast that we often have to move on rather than
go back to correct errors.
One example of a problem caused by the growth of
knowledge is the enzymatic mechanism for lysozyme.
For over 50 years the accepted mechanism involved an
ion pair intermediate. It was not until 2001 that new
experiments showed that the intermediate was instead a
covalent glycosyl enzyme.26 As researchers are challenged
to stay up to date with the scientific literature and SPRs
to continually update their information, nonexpert users
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would be forgiven for thinking the ion-pair mechanism
was still the definitive one (especially as this is the mechanism shown in many text books as well). Such examples
raise a number of questions for our community and our
users: Can we ever say that we know the correct mechanism of an enzyme? Can we ever hope to keep up with
the frontier of scientific discovery? Further, even if the
new information has been published, will it be incorporated into any database resources and then propagated
throughout the many different SPRs? Possibly not—as
the key to keeping databases up-to-date requires that
curators (or users, or text mining robots) go back over
the literature again and again to identify changes, new
discoveries, and what information has become obsolete.
Ideally, we need an exceptional solution to accurate and
automated updating of all relevant databases, even
including those that are deeply dependent on specialized
knowledge within a field.
Another common error found in protein sequence
analysis is the misannotation of a protein due to its
modular (or multidomain) structure. For example, the
carbohydrate-binding module (CBM) in carbohydrateactive enzymes is frequently found appended to catalytic
domains belonging to various families, including
domains of unknown function. A best BLAST27 hit
matching only the CBM often leads to erroneous annotation of the adjacent domain. This is because the matched
domain is often used to annotate the function of the
entire protein, not just the portion found via BLAST. In
many cases, such errors can only be identified when
researchers go back to carefully examine specific cases in
detail. For example, the aminotransferase-related enzyme
(UniProtKB: B8NM72) was ultimately found to be
involved in synthesis of a ribosomal peptide, rather than
acting as a nonribosomal peptide synthetase as previously
thought.28 Such annotation transfer errors can often
lead researchers astray and highlights why expert manual
annotation is so essential for SPRs.
Although over-prediction, transferring annotation
from one annotated protein to another of unknown
function using relatively lax parameters, has the advantage of increased data coverage, it can lead to many erroneous function predictions. Such annotation errors can
be further compounded by “proof by repetition”; the
assumption that the most numerous annotations are the
correct ones.29 Such errors can be protected against by
“under annotation,” that is, transferring data only when
we have the highest confidence that it is accurate, for
example, in requiring not only a high confidence BLAST
score, but also in having the active site profile fully
matched. These protocols often lead to significantly fewer
annotations being assigned, but the quality of the annotation transfer is much better. In both cases, annotation
transfer is further complicated by the fact that a protein’s
function can be defined as the molecular/chemical role
(e.g., a specific serine kinase) or the broad biological pro-
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cess the protein mediates (e.g., mediating the coagulation
of blood). Generally, it is quite difficult to decipher the
biological role of a protein in the physiological context
using computational methods and therefore such predictions should be used with caution. Nowadays a BLAST
search of the nonredundant protein database of NCBI
(RefSeq) or on UniProtKB often identifies a large number of similar proteins originating almost exclusively
from genome sequencing (that is, these proteins have
had no characterization performed). Close examination
of the names attributed to these proteins shows that they
are both heterogeneous and transmitted from one to
another via automated processes (creating a mess that is
increasingly difficult to discern and fix).
Many protein homologues lack one or more critical
residues, making them functionally inactive, another
aspect of annotation transfer that may lead to erroneous
annotation. These proteins may be biologically relevant
but with another function, or on the other hand, the
missing residues could be artefacts caused by gene assembly errors. Other typical gene assembly errors lead to the
prediction of putative proteins where the wrong initiating methionine has been identified, or where exons have
been omitted. Although such errors may be subsequently
corrected, finding the time to back-check for these types
of errors requires more resources than are available to
many SPR curators.
Fixing annotation errors and propagating
data
Once an error is identified, how do we fix it? Many
resources, such as UniProtKB and RefSeq, have mechanisms for users to report problems so that annotation
errors can be corrected. Additionally, specialized resources
have been developed to help address this issue and provide
at least some reannotations (such as PDB_REDO30 for
PDB atomic coordinates). However, many others, such as
the Protein Data Bank (PDB),31 GenBank,32 the European Nucleotide Archive (ENA),33 and the DNA Data
Bank of Japan (DDBJ),34 lack these procedures as they are
primary repositories that are designed to archive original
data. SPRs tend to have their own policies for correcting
errors that are relevant to the specific nature of each
resource. Many welcome (and need) input from expert
users in order to identify and correct data errors.
Identifying the error is only the first step. Once we
know an error exists, how do we propagate the fix
through all the SPRs that utilize the original entry? The
provenance of a datum is often difficult to identify. Have
the database curators taken information directly from
UniProtKB, or RefSeq, or from the primary literature?
Maybe they took it from another resource, but where did
that resource’s curators get it from? While such repurposing of data is common place, it is a good way to
propagate annotation errors. A better solution might be
Creation and Maintenance of Protein Resources
for all primary data to be stored in a common archive
resource so that niche or derived databases could provide
pointers to the original information that could then be
expanded on demand. However, such an annotation
archive would be challenging to implement on a wide
scale.
Another promising approach used by some resources
(e.g., the Gene Ontology (GO),35 UniProtKB, and some
SPRs) is to use the concept of “evidence,” sometimes in
combination with the use of the Evidence Code Ontology (ECO),36 a structured and controlled vocabulary for
evidence in biological research. Used in the context of
protein function annotation, evidence codes allow for
evidence not only to have a type (e.g., inferred from electronic annotation), but to have a source (e.g., a specific
resource), providing users with an effective way to judge
the confidence with which to judge an annotation. Other
SPRs are starting to follow suit although populating a
resource with this sort of “back annotation” can be a
long and often complicated process as all the data must
be cross-checked and back-edited. That being said, if we
are ever to propagate annotation “fixes,” the ability to
follow data back to their source is going to be critical,
suggesting the value of using ECO (or something similar) moving forward.
With the ever increasing volume of data, how do we
grow and maintain our resources responsibly, especially
in the context of the misannotation challenge? Specialist
curation (by individuals who are highly trained to a particular resource and/or in a particular field) is always
going to be critical because databases that include a high
degree of cross-check and human curation provide significant added value over simple repositories or metaresources/hubs that are especially prone to propagation
of misannotation. For example, the IUPHAR/BPS Guide
to PHARMACOLOGY uses expert curators for particular
protein “receptor” types that are linked to subcommittees of experts who ensure data quality. This approach
allows experts to keep their field “clean” and to benefit
from highly cited publications37 rather than using “data
trawling” which can lead to misleading information
being propagated.
The user’s role in expanding SPR data
coverage
Users of the SPRs are going to become increasingly
important to correcting errors and growing SPRs in the
future. For example, expert users are in a position to
inform SPRs of errors that they have spotted or to contribute new entries based on their experiments and/or
publications. Depending on an evaluation of the evidence
provided, the resource can then update the entry. In the
experience of the attendees at the retreat, the major hurdle to adopting user based annotation methods is educating users about the benefits of contributing their
information to database resources versus the effort of
creating the annotation themselves (commonly referred
to as the “tragedy of the commons”38). One route to
achieving more user input (such as that taken by the
international crystallographic community) is to require
data entry before the results can be published. Without
the support and enforcement by the journals, however, it
is not practicable to capture functional information efficiently in this manner. On the bright side, the level of
detail repositories require of their depositors need not be
onerous. For example, including the EC number along
with a sequence accession number for an enzyme would
represent enormous progress that would allow SPRs and
larger resources alike to incorporate research results
keyed to those common identifiers. The flip side of
encouraging annotation and error correction contributions by users is that the manual incorporation of this
information could quickly outgrow a resource’s ability to
keep up. Again, user submissions enabled via structured
information formats supported by the journals would
offer progress toward more automated solutions. The
International Society for Biocuration (http://www.biocurator.org) is an active proponent in bringing scientists,
curators and journals together with a view to enable user
submissions. The annual International Biocuration Conference is a great opportunity for these groups come
together to discuss the challenges involved.
A different route to maintaining data quality is the use
of the Wikipedia model. Rfam39 and Pfam17 both utilize
this model to populate the respective databases with
Wikipedia pages created by authors. Although both of
these approaches are promising, general application of
this model awaits answers to several basic questions:
which resources become the primary repositories of usercontributed data? How do we deal with overlapping
resources? When an old resource is retired, who takes on
its data? Will one resource become the ultimate one for
all protein annotations, which are then used and elaborated upon by SPRs? Will all journals agree to the process? Will the annotation process be both simple and
complete enough that authors find benefit to the process? There are no simple answers to these questions, but
as the amount of data grows, many aspects of protein
research would benefit if SPR developers, users and publishers begin to work together in developing a common
plan for moving forward.
Weathering the data deluge
Even the best resource must keep its information upto-date, and in this omics era possibly the biggest challenge we face is the sheer volume of data currently available, along with its projected growth at a near
exponential rate. There is also a constant growth in the
number of data-sources. UniProtKB and RefSeq have
approximately 89 million and 47 million entries,
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respectively, as of November 2014, of which just over
half a million are manually annotated or reviewed in
each database and over 70% of these reviewed proteins
are annotated via similarity to a protein of known function. For every bit of information on a single protein
that exists, there are even more proteins for which we
have no data, save a primary amino acid sequence. SPRs
have two important roles to play in weathering this data
deluge: one is to provide novel annotation and understanding in their field of expertise and the other is to
provide online tools to access those annotations. Additionally, SPRs must determine what information to submit to larger and more general resources, and which to
glean from other data resources. These roles, in turn, aid
resources such as RefSeq and UniProtKB in extending
and improving their predicted annotations. A good SPR
should know where its strengths lie and clearly distinguish the primary annotations for which they are the
unique source from those data that come from other
resources.
Adoption of best practices
Along with annotation input from experimental users,
the Retreat discussion also suggested that the SPRs could
benefit from best practices that have been developed.
However, for any one resource to be useful to another,
the language that they both use needs to be standardized.
One such exercise in standardization was the EMBRACE
project (http://www.embracegrid.info/40) which worked
to integrate major databases and software tools in bioinformatics, using existing methods and emerging Grid
service technologies. Some resources already use the
same language, conceptually facilitating the exchange of
information between them. For instance, OMPdb uses
the commonly accepted family classification system of
Pfam. But generally, what one resource means by the
term «family» or «superfamily» might not be what
another means. For example, the SFLD definition
requires that the proteins not only be evolutionarily
related, but that they have a conserved chemical aspect
to their function. TIGRFAM, on the other hand, only
requires evolutionary relatedness.
While we are not advocating that all resources use an
identical language (biology is nothing if not messy, so
a term in one field will not directly translate to
another), there needs to be a way to both establish and
translate concepts. Ontologies are certainly the most
robust method to do this, and SPRs need to define
their language and concepts clearly so that mapping is
possible between the different SPRs. Although the Gene
Ontology (GO) is probably the most widely known
ontology in the field of bioinformatics, a PubMed
search for “ontology” in the title of a article yields
almost 1500 hits (almost 500 of which involve GO).
Especially for some key concepts of biochemistry and
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biology, relevant to SPRs, the capability exists to link
data across resources that share similar data. The ontology repositories, such as BioPortal41 and the OBO
Foundry,42 offer a good way to find an ontology that
will help describe specific types of data by collecting as
many biological ontologies as possible into a single
location. At the very least, an understanding of the
terms used by various resources will allow SPRs to map
data between one another, benefitting both our curators
and users.
Resource longevity
The 2014 Nucleic Acids Research database issue contained 58 new databases and updates to 123 existing databases, growing the total number of databases represented
in the online collection of molecular biology databases
(http://www.oxfordjournals.org/our_journals/nar/database/
c/) to 1552 in 2014.23 It is quite easy to create a database,
and many databases are created as part of a PhD or Masters projects, but with no plan for future maintenance of
the information. Even for more established SPRs, it
remains hard to maintain such resources over many years.
A 2008 study found that almost 40% of database URLs
published in journals were no longer regularly available.43
“Zombie databases” are not maintained past the original
publication for reasons that range from lack of interest or
funding to a career move by the creator. Over time, these
may become unreliable or even misleading by failing to
keep current with the field, including naming conventions,
links to other databases, or even browser compatibility,
and eventually are taken down. One answer to the problem of longevity is greater integration. An example of such
an approach is InterPro,44 a resource that integrates eleven
different protein domain and family resources into a onestop-shop. The member databases still retain their own
identity, data, and role in the wider community while
InterPro provides access to their annotations (and expertise) through a single website. The caveat to the inclusion
of a new SPR within InterPro is that the source database
must have sequence analysis methods that are reproducible
and scalable, so is unlikely to be suitable for all SPRs, for
example, CAZy, where sequences are annotated on an individual basis. One of the roles performed by InterPro is the
provision of the annotations produced by its member
databases to UniProtKB on a monthly database, such that
annotations are up-to-date with the source member database and all sequences found in UniProtKB. The advantage
to the InterPro user is the ability to view all the different
annotations in a single resource. To be able to view broadand fine-grained annotation in a single interface is highly
efficient, so in this respect it is arguable that the whole
(InterPro) is greater than the sum of its parts (member
databases).
Similarly, Pfam (which can be considered both a SPR
and a reference database) uses the annotations found in
Creation and Maintenance of Protein Resources
many of the SPRs as either starting points for generating
new entries and/or for annotating existing Pfam entries.
For example, most of the peptidase families in Pfam
have been derived from or annotated using MEROPS
(note that Pfam does not contain any of the fine-grained
subfamily annotations found in MEROPS). In many
ways SPR data integration into Pfam parallels that of
data integration of member databases into InterPro;
however, the attribution to the source SPR is less obvious
than with InterPro. Also, Pfam will derive its own profile
hidden Markov model for the entry and possibly supplement the SPR annotation. There is also the risk that
smaller SPRs will be subsumed by Pfam and in so doing
will reduce traffic to the individual resources. Furthermore, as annotations of both proteins and domains are
updated in both in the literature and SPRs, there is no
rigorous mechanism in the Pfam production software
pipelines to identify and reconcile the differences
between Pfam and the SPRs. However, a major advantage
of Pfam is its wide use within the scientific community.
Moreover, it is a founder member database of InterPro
and is used within CDD.45 Thus, the information in
Pfam, both curated by Pfam and derived from the SPRs,
is propagated to a broad audience.
There are several other examples of consortia that
work toward greater integration of protein resources, and
although not formally represented at the inaugural SPR
meeting, these have proven to be exceedingly useful. Two
such examples are the HUPO Proteomics Standards Initiative46 and the International Molecular Exchange
(IMEx) Consortium of Protein-Protein Interaction databases.47 The IMEx Consortium is an excellent example
of where coordination of a set of related databases has
led to standardization and improved interoperability.
(UniProtKB was the only member of this consortium
represented at the SPR meeting.)
For many small SPRs attending our retreat, a continuing challenge to longevity is obtaining funding. In contrast, this is not a consideration for the Histone Database
and the RefSeq Database as these are supported by intramural funds at the National Institutes of Health. Several
models currently exist for funding SPRs. These include:
Self-funding, that is, they are maintained using funds
provided to the research group by the board of directors
of the home institute for normal running of the group,
for example, GPCRDB and DSSP,48 grant agency funding, for example, the SFLD is currently supported by
NIH and NSF grants, user-based funding (commercial),
for example, KEGG,49 which, due to lack of other funding resources, is now forced to operate via paid licensing
fees, and user base “funding” (public), for example, the
Little Skate Genome Project,50 which held many jamborees to annotate the skate genome, minimizing the need
for a large curator and bioinformatics staff employed by
the resource. It is our job, as a community of SPRs to
help one another and to listen to our users. Furthermore,
those of us in the SPR community need to work together
to minimize duplication of effort, helping one another to
maintain quality as well as quantity so that our users
have the best possible data from which to work. We also
need help from our user communities, without whom
our resources cannot hope to thrive. Finally, as funding
for the Wellcome Trust Retreat was of necessity limited
to a small group of database resources, we would like to
encourage any researchers that run their own SPRs to
join our mailing list (https://listserver.ebi.ac.uk/mailman/
listinfo/sprn) and contribute to further discussion about
the issues described in this brief report, including greater
data interoperability, standardization, and consolidation.
CONCLUSIONS
SPRs are critical to biological research in this omics
age and we can all benefit from sharing the many different technical approaches that have been developed. This
is especially true for meeting the challenge of creating
user-friendly web interfaces and supporting complex
queries, both of which are difficult to develop and maintain. Such technical developments represent a high burden on many SPRs and their requirements can quickly
grow beyond an SPR’s interest and expertise. Still, many
of us have developed similar data structures which could
allow us to share methods for developing web interfaces
and adoption of external tools. A further useful extension would be an agreement among SPRs to make their
data available to other resources in some fairly standard
or well defined format, attended by assurance of the
quality and provenance of the contributed data/annotations (e.g., when the data were last updated and where
they came from). Finally, greater coordination and collaboration between databases is going to be even more
important with the ever growing amount of data available. Here, a synergy between SPRs and reference databases will be essential, with the SPRs improving the
accuracy and quality of their data and the reference databases integrating these data for broader dissemination to
the research community.
ACKNOWLEDGMENTS
The participants of the inaugural SPRN meeting thank
the Wellcome Trust for funding the meeting and Alex
Bateman and Patricia Babbitt for organizing it.
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